用神经辐射场融合可见光与热成像，免去繁琐校准。
MultiBARF: Integrating Imagery of Different Wavelength Regions by Using Neural Radiance Fields
- 基于BARF扩展，自动合成多波段图像与深度图
- 实验证明可成功叠加可见光与热成像双通道
- 适合无传感经验的用户快速融合异源影像
光学传感器应用在数字化转型中日益普及。将观测数据与真实位置关联并整合不同图像传感器，对提升应用的实用性和效率至关重要。然而，尝试不同传感器组合所需的数据准备需要较高的传感与图像处理专业知识。为降低非专业用户的使用门槛，我们提出了MultiBARF。该方法通过神经辐射场（NeRF）技术，在指定视角下直接合成两组不同传感器的图像与深度图，替代传统的配准与几何标定步骤。本方法在可见光与热成像数据上的实验表明，能够成功将两种传感器图像的色彩通道叠加至同一NeRF空间中。
原文摘要 · Abstract (English)
Optical sensor applications have become popular through digital transformation. Linking observed data to real-world locations and combining different image sensors is essential to make the applications practical and efficient. However, data preparation to try different sensor combinations requires high sensing and image processing expertise. To make data preparation easier for users unfamiliar with sensing and image processing, we have developed MultiBARF. This method replaces the co-registration and geometric calibration by synthesizing pairs of two different sensor images and depth images at assigned viewpoints. Our method extends Bundle Adjusting Neural Radiance Fields(BARF), a deep neural network-based novel view synthesis method, for the two imagers. Through experiments on visible light and thermographic images, we demonstrate that our method superimposes two color channels of those sensor images on NeRF.
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